A meta-learning-based AutoML method for aero-engine rolling element bearing fault diagnostics

Rolling element bearings are crucial in aero-engines, and accurate fault diagnostics can avert major aircraft accidents by enabling timely detection and maintenance. Machine learning (ML) excels in fault diagnosis but requires extensive time for model architecture tuning and hyperparameter selection, challenging operational staff and hindering implementation. Recently, Automated Machine Learning (AutoML) has emerged to tackle hyperparameter optimization in rotating machinery diagnostics. Traditional AutoML techniques, such as Bayesian optimization, grid search, and random search, automate model selection and tuning. Yet, they are underutilized in aero-engine bearing fault diagnosis, where research remains limited. To boost automation, this study proposes a meta-learning-driven AutoML framework. By incorporating health indicators, which reflecting rotating machinery fault states as meta-features, the framework precisely recommends optimal architectures and hyperparameters, expediting intelligent diagnostic system deployment. Experiments on aero-engine bearing faults show this method outperforms other ML approaches in classifying health indicators, time-domain waveforms, and frequency-domain vibration spectra. It exhibits remarkable robustness in noisy conditions and small-sample scenarios.